Download - An attempt to use literature curated pathway DBs Charles Vaske Stuart Lab Meeting May 6th, 2009
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An attempt to use literature curated
pathway DBs
Charles VaskeStuart Lab Meeting
May 6th, 2009
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Structured Pathways
•Lots of cancer research/genes/data
•Subsequently, we know a lot about pathways active in cancer
•Can we use this structured knowledge?
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Modeling clinical samples
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Use in clinical samples
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Outline1.Get pathways (ugly, 50%-95%
done)
2.Convert to graphical model
3.Add evidence from patient
4.Infer the value of hidden variables(i.e. Apoptosis, Chemotaxis)
5.Solve cancer (finally)
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•Proteins
•Complexes
•Abstract procceses
•Reactions/modifications/translocations
•Activation vs. participants
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BioPAX•Based on OWL
Web Ontology Lang.
•Based on RDFResource Desc. Format
•Not human-readable
•Must use tools!
•I love to complain about it
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•Three levels (versions), people only use level 2 (I think)
•Defines “things” which have various properties, including a “class”
•Each “thing” is a URI, which looks like a URL
BioPAX
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RDF/OWL/BioPAX Tools
•Protege: from Stanford, designed more for creating a BioPAX more than looking at “data” in that “format”
•SPARQL/roqet: sort of like SQL for RDF. Don’t use XML tools, you may miss things due to variations in serializations.
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Caveat•All this dense typing
and formating is extremely expressive
•However, the amount of expression impedes programmatic understanding
•Test, test, test
This shows the “transcription” of a complex. The meaning is obvious to a human, but befuddling to my naive scripts.
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PREFIX bp: <http://www.biopax.org/release/biopax-level2.owl#>SELECT ?goname ?entity ?activationWHERE { ?mod bp:CONTROL-TYPE ?activation . ?mod bp:NAME ?goname . ?mod bp:CONTROLLED ?reaction . ?reaction bp:RIGHT ?pep . ?pep bp:PHYSICAL-ENTITY ?entity}
Example query: find abstract processes
that are parents
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•Started by finding the proper queries to extract interactions, names, parts of complexes...
•Want a simple tab-delimited format:
Parsing
abstract metaphaseabstract mitosiscomplex AurC/AurB/INCENPprotein H3F3Aprotein AuroraBprotein AuroraCprotein INCENPAurC/AurB/INCENP H3F3A -a>mitosis H3F3A -ap> metaphase AurC/AurB/INCENP -ap>AuroraB AurC/AurB/INCENP component>INCENP AurC/AurB/INCENP component>AuroraC AurC/AurB/INCENP component>
Entity Definitions
Entity Interactions
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abstract metaphaseabstract mitosiscomplex AurC/AurB/INCENPprotein H3F3Aprotein AuroraBprotein AuroraCprotein INCENPAurC/AurB/INCENP H3F3A -a>mitosis H3F3A -ap> metaphase AurC/AurB/INCENP -ap>AuroraB AurC/AurB/INCENP component>INCENP AurC/AurB/INCENP component>AuroraC AurC/AurB/INCENP component>
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My hopeful monster•Makefile
converted to executable script
•A bit experimental
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$MAPDIR/Data/Pathways
•Early, molten stage, but useful
•Human/NCIPID has NCI pathways
•Human/KEGG has early KEGG attempts
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Pathway stats132 Pathways
3766 Unique Entities
7569 Entity instances
10182 Unique Interactions
Entity Breakdown
1742 protein1638 complex296 abstract90 chemical
Interaction Breakdown
2619 -a> (activation)278 -a| (inhibition)874 -ap> (abstract)103 -ap|528 -t> (transcription)104 -t|5676 component>
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Outline1.Get pathways (ugly, 50%-95%
done)
2.Convert to graphical model
3.Add evidence from patient
4.Infer the value of hidden variables(i.e. Apoptosis, Chemotaxis)
5.Solve cancer (finally)
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Aurora C Factor Graph
abstract metaphaseabstract mitosiscomplex AurC/AurB/INCENPprotein H3F3Aprotein AuroraBprotein AuroraCprotein INCENPAurC/AurB/INCENP H3F3A -a>mitosis H3F3A -ap> metaphase AurC/AurB/INCENP -ap>AuroraB AurC/AurB/INCENP component>INCENP AurC/AurB/INCENP component>AuroraC AurC/AurB/INCENP component>
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Aurora C Evidence
AuroraB genome 0.87082AuroraB mRNA 0.37673AuroraC genome 0.170729AuroraC mRNA 0.045578INCENP genome -0.082277INCENP mRNA -0.060272H3F3A genome -0.411328
• Data points are signed, log p-values
• Right now, I discretize into up/down/same at 0.05 level
• Therefore, many patients look “identical” on hidden variables
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Aurora C Inference
•using the package libDAI, which implements many approximate inference algorithms (and exact)
•Using exact at the moment
•128 patients, 132 pathways ~ 2 hours
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Prelim. Pathway results•2 data sets
•Glioblastoma 224 samples
•Ovarian Cancer 128 samples
•Still working out kinks in pipeline
•Not satisfied with data treatment